CN114429157A - Method for analyzing terrestrial physical signal characteristics - Google Patents

Method for analyzing terrestrial physical signal characteristics Download PDF

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CN114429157A
CN114429157A CN202210081786.9A CN202210081786A CN114429157A CN 114429157 A CN114429157 A CN 114429157A CN 202210081786 A CN202210081786 A CN 202210081786A CN 114429157 A CN114429157 A CN 114429157A
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filter
geophysical
ultra
frequency
signal
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徐亚
南方舟
黄松
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Institute of Geology and Geophysics of CAS
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Institute of Geology and Geophysics of CAS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis

Abstract

The invention provides a geophysical signal characteristic analysis method, which comprises the following steps: according to a specific geophysical signal, the bandwidth of the signal is determined according to the sampling interval and the length of the geophysical signal, a filter with specific main frequency and the bandwidth is designed, a filter bank is constructed, after the signal and the filter in the filter bank are subjected to filtering operation, the result is subjected to geometric averaging to obtain the output signal of the filter bank under the specific main frequency, and the characteristic signals of the geophysical signal at different frequencies can be further obtained by carrying out the operation on the filter banks of a plurality of main frequencies. The invention can effectively extract the effective frequency band information of the data, and greatly improves the resolution ratio compared with the traditional method.

Description

Method for analyzing terrestrial physical signal characteristics
Technical Field
The invention relates to the technical field of geophysical exploration and exploration, in particular to a geophysical signal characteristic analysis method which is used for analyzing and extracting signal characteristics such as earthquake, well logging, electrical methods, electromagnetism, gravity, magnetic force and the like.
Background
Geophysical data is typically a time-sampled or spatially sampled signal in which there is abundant information about various features within the earth. A geophysical signal analysis method based on a time-frequency analysis technology is one of effective methods for obtaining geophysical signal characteristics, such as extracting weak reflection signals of seismic exploration deep strata, analyzing the accurate time of natural seismic events, analyzing deep low-frequency information in gravity anomaly, extracting high-frequency magnetic anomaly characteristics caused by shallow igneous rock masses and the like.
Geophysical signals generally belong to nonlinear non-stationary signals, and traditional signal time-frequency analysis methods include short-time Fourier transform, wavelet transform and the like, have certain time (space) resolution and frequency resolution, and are widely applied to geophysical data processing and analysis. And by the inaccurate measurement principle of Heisenberg, the method cannot obtain optimal resolution in time and frequency at the same time, and the identification of signal characteristics is influenced to a certain extent.
Disclosure of Invention
The invention aims to provide a geophysical signal feature analysis method, which can clearly see the distribution of effective information in data in a time-frequency energy diagram, improve the signal time-frequency feature resolution capability and support the feature analysis and extraction of extracted signals.
A geophysical signal characteristic analysis method comprises the following steps:
the method comprises the following steps that firstly, the frequency band range of a signal is determined according to the sampling interval and the signal length of geophysical data;
the geophysical data in the first step include gravity, magnetic, electrical, electromagnetic, seismic, and logging signals. The sampling interval of the geophysical data is regular sampling or irregular sampling, and the length of a data signal is not limited.
Secondly, selecting a proper filter function as a fundamental order filter function, setting the dominant frequency of the filter as f and setting the bandwidth as Bc；
The fundamental filter function is a filter determined by the center frequency and bandwidth parameters.
The center frequency and the bandwidth of the fundamental filter function are determined by a relational expression or defined by a user.
The basic order filtering function is a wavelet basic function and a filter function with band-pass property.
Thirdly, according to the selected fundamental filter function, the bandwidth of the selected fundamental filter function is changed to form different filter functions, and the series of filter functions are constructed into an ultra-small wave base;
the ultra-small wave base is a filter bank. The main frequencies of the filter functions of the filter bank are consistent.
The filter function for constructing the ultra-wavelet base is determined by selecting different bandwidth parameters from the basic order filter function. The bandwidth parameters are realized through the bandwidth parameters of the fundamental filter according to an equal ratio sequence or an equal difference sequence.
Fourthly, performing ultra-wavelet transform on the geophysical signals, namely performing convolution operation on the geophysical signals and ultra-wavelet basis respectively to obtain time-frequency signals of different filtering functions of the ultra-wavelet basis, and performing geometric averaging on output signals to obtain total output signals after the ultra-wavelet basis operation, namely ultra-wavelet transform signals corresponding to basic frequency of the filtering function of the basic order;
fifthly, changing the main frequency of the fundamental filter, and repeating the second step to the fourth step to obtain an ultra-small wave base and a corresponding ultra-small wave conversion signal under different main frequencies;
and sixthly, obtaining output signals with different main frequencies according to the steps, arranging and outputting the output signals according to the main frequencies to obtain a time-frequency energy distribution map of the geophysical signals, and analyzing the characteristics of the geophysical signals according to the time-frequency distribution.
The invention constructs the ultra-small wave base based on the idea of filter bank design, realizes the optimal resolution of the signal in time domain and frequency domain simultaneously by ultra-small wave transformation, and improves the identification capability of the signal characteristics.
Drawings
FIG. 1 is a flow chart of an acquisition protocol of the present invention;
FIG. 2a shows the result of the ultra-small wave transformation in the embodiment;
FIG. 2b is the result of a short time Fourier transform of example seismic data;
FIG. 2c shows the result of an example wavelet transform of seismic data;
FIG. 3 illustrates the application of the present invention to weak seismic signal extraction.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention. Additionally, the described embodiments are merely illustrative of the present invention and are not intended to be limiting.
The invention aims to analyze the geophysical signal characteristics by the method, improve the effective signal identification capability and increase the accuracy of signal identification.
The present invention will be described in detail below. A geophysical signal characteristic analysis method, as shown in fig. 1, includes the following steps:
firstly, confirming the sampling frequency and the data length of the geophysical data according to the actually processed geophysical data;
secondly, a reasonable filtering function is analytically calculated according to a specific data type and is used as a super wavelet base, in the embodiment, a Gabor function is taken as an example (formula 1), wherein f represents the center frequency, c is the period number, and B iscT represents time as the effective bandwidth;
Figure BDA0003486315330000021
and thirdly, changing the bandwidth of the selected fundamental filter function to form different filter functions according to the selected fundamental filter function, wherein the series of filter functions form an ultra-wavelet basis. I.e. the ultra-small wave basis is defined as a series of sets of filter functions having the same center frequency and different filter bandwidths. Equation 2 represents the wavelet basis, where f represents the center frequency of the wavelet basis, o represents the order of the wavelet basis, which is the bandwidth parameter of the filtering function of the order, c1,c2,...,coA parameter representing the bandwidth of each ultra-small wave base,
SLf,o＝{ψf,c∣c＝c1,c2,...,co} (2)
fourthly, performing ultra-small wave transformation on the geophysical signal x, firstly calculating filtering signals of the geophysical signal x and the ultra-small wave base according to a formula 3 by using filtering functions with different bandwidths, and calculating all filtering signals of the ultra-small wave base according to a formula 4 to obtain output signals of the ultra-small wave transformation, wherein x represents a complex convolution operator;
Figure BDA0003486315330000031
Figure BDA0003486315330000032
a fifth step, up to this point, a result R [ SL ] of the ultrasmall wave transformation of the center frequency has been obtainedf,o]. And (4) taking the fundamental filter with different central frequencies, repeating the second step to the fourth step, and performing cyclic calculation to obtain the time-frequency characteristic signals of the geophysical signal x obtained by the ultra-small wave base with different main frequencies.
And sixthly, summarizing the time-frequency characteristic signals obtained by the ultra-small wave transformation of different main frequencies to obtain a time-frequency characteristic distribution map of the geophysical signals, wherein the time-frequency characteristic distribution map is used for analyzing and extracting the characteristics of the geophysical signals.
Wherein, the third step is an ultra-small wave base c1Is more important to select, c1When the time is small, the resolution of the whole ultra-small wave transformation is increased, the number o of ultra-small wave bases is increased, the precision of the whole transformation is increased, and the calculation amount is increased; c. C1,c2,...,coCan be selected according to c1As a base geometric or heterological sequence, i.e. ci＝i*c1，i＝1,...,o；
The reason why the complex convolution occurs in the fourth step is that the Gabor wavelet shown in equation 1 is a complex wavelet, and therefore, the complex convolution is adopted in equation 3 while the multiplication is required
Figure BDA0003486315330000033
If other non-complex ultrasmall wave bases are used, normal convolution is sufficient and multiplication is not needed
Figure BDA0003486315330000034
Right of (1)Weighing;
the maximum value of the central frequency of the circular calculation in the fifth step is determined by the sampling frequency, is less than half of the sampling frequency, and is generally selected according to specific geophysical data;
wherein, the sixth step of performing time-frequency analysis on the time-frequency characteristic spectrum generally comprises the following two steps:
(1) comparing the time-frequency energy diagram of wavelet transform, confirming the accuracy of the transform, if the resolution of the obtained time-frequency energy diagram is equivalent to that of the wavelet transform, returning to the third step, and reselecting c1And o, calculating to improve the resolution of the time-frequency energy diagram;
(2) and extracting corresponding effective information according to the optimal result.
Further description is given below by way of example.
Taking a land station seismic event signal as an example, the method is used for extracting the seismic event and is realized by the following steps:
firstly, according to specific seismic data, confirming that seismic signals with the sampling frequency of 50Hz, namely below 25Hz can be acquired;
secondly, selecting a Morlet function as a filtering function, namely an ultra-small wave base,
Figure BDA0003486315330000041
Figure BDA0003486315330000042
c0＝0，c1＝1，c1,c2,...,cocan be selected according to c1As a base geometric or heterological sequence, i.e. ci＝i*c1I is 1, the degree o is 0-4, and the transformation result is shown in fig. 2 a;
thirdly, performing short-time Fourier transform (figure 2b) and wavelet transform (figure 2c) on the seismic data;
fourthly, comparing the resolutions of the three time-frequency analysis methods: the three transformations can reflect accurate seismic event time, and the resolution of 2-4 order ultra-small wave transformation on the seismic time is better, so that the feasibility of the acquisition method is proved.
The method has the advantages that the proper parameters are adjusted, the reasonable filter function is selected to construct the super wavelet basis, the high-resolution time-frequency energy graph can be obtained, and the accuracy of extracting effective time-frequency information is improved. Fig. 3 shows the background noise signal of a land station, and the signal extraction method of the invention can be used for accurately identifying weak seismic signals which are difficult to be identified by naked eyes, thereby greatly improving the accuracy of seismic event extraction.
It should be apparent that the above examples are merely illustrative of specific implementations of the present invention. The present invention is described in detail with reference to the accompanying drawings. It will be apparent to those skilled in the art that other variations and modifications may be made in the invention without departing from the spirit or scope of the invention as defined in the following claims. Obvious variations or modifications of this invention are within the scope of the invention as claimed.

Claims (10)

1. A geophysical signal feature analysis method is characterized by comprising the following steps:
the method comprises the following steps that firstly, the frequency band range of a signal is determined according to the sampling interval and the signal length of geophysical data;
secondly, selecting a proper filter function as a fundamental order filter function, setting the dominant frequency of the filter as f and setting the bandwidth as Bc；
Thirdly, according to the selected fundamental filter function, the bandwidth of the selected fundamental filter function is changed to form different filter functions, and the series of filter functions are constructed into an ultra-small wave base;
fourthly, performing ultra-wavelet transform on the geophysical signals, namely performing convolution operation on the geophysical signals and ultra-wavelet basis respectively to obtain time-frequency signals of different filtering functions of the ultra-wavelet basis, and performing geometric average on output signals to obtain total output signals after the ultra-wavelet basis operation, namely ultra-wavelet transform signals corresponding to the basic frequency of the basic-order filtering function;
fifthly, changing the main frequency of the fundamental filter, and repeating the second step to the fourth step to obtain an ultra-small wave base and a corresponding ultra-small wave conversion signal under different main frequencies;
and sixthly, obtaining output signals with different main frequencies according to the steps, arranging and outputting the output signals according to the main frequencies to obtain a time-frequency energy distribution map of the geophysical signals, and analyzing the characteristics of the geophysical signals according to the time-frequency distribution.
2. The method of claim 1, wherein the geophysical data in the first step comprises gravity, magnetic, electrical, electromagnetic, seismic, and logging signals.
3. The geophysical signal characteristic analysis method of claim 1 wherein the geophysical data sampling interval in the first step is regular sampling or irregular sampling, and the data signal length is not limited.
4. The method as claimed in claim 1, wherein the fundamental filter function in the second step is a filter determined by the center frequency and the bandwidth parameter.
5. The method of claim 4, wherein the center frequency and the bandwidth of the fundamental filter function are determined by a relational expression or defined by a user.
6. The method according to claim 1, wherein the second step comprises a step of filtering the wavelet basis function with a band-pass filter function.
7. The method according to claim 1, wherein the ultra-small wave basis in the third step is a filter bank.
8. The method of claim 7, wherein the filter bank has filter functions with uniform dominant frequencies.
9. The geophysical signal characteristic analysis method of claim 1, wherein the filter function for constructing the super wavelet basis in the third step is determined by selecting different bandwidth parameters from the basic order filter function.
10. The geophysical signal feature analysis method of claim 7 wherein the bandwidth parameters are implemented as geometric series or arithmetic series using fundamental filter bandwidth parameters.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115390140A (en) * 2022-08-24 2022-11-25 中国科学院地质与地球物理研究所 Method and system for extracting seismic attribute body

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115390140A (en) * 2022-08-24 2022-11-25 中国科学院地质与地球物理研究所 Method and system for extracting seismic attribute body

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